File size: 3,048 Bytes
d566fee a3cb4b9 ee36b3c ba699eb d566fee a3cb4b9 d566fee a3cb4b9 ee36b3c d566fee ee36b3c 6704e8f 4a077d0 6704e8f a3cb4b9 ba699eb a3cb4b9 ee36b3c d566fee 6704e8f 4a077d0 a3cb4b9 6704e8f f132889 d566fee 6704e8f d566fee 4a077d0 d566fee 23fec88 41dcd30 d566fee ba699eb 2825722 ba699eb 2825722 d566fee 910566d 6704e8f 8a69f2c 6704e8f d566fee 910566d 6704e8f 910566d ba699eb d566fee |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 |
import gradio as gr
import tensorflow as tf
import numpy as np
from PIL import Image
import google.generativeai as genai
import os
import markdown2
# Load the TensorFlow model
model_path = 'model'
model = tf.saved_model.load(model_path)
# Configure Gemini API
api_key = os.getenv("GEMINI_API_KEY")
genai.configure(api_key=api_key)
labels = ['cataract', 'diabetic_retinopathy', 'glaucoma', 'normal']
def get_disease_detail(disease_name):
prompt = (
f"Diagnosis: {disease_name}\n\n"
"What is it?\n(Description about {disease_name})\n\n"
"What causes it?\n(Explain what causes {disease_name})\n\n"
"Suggestion\n(Suggestion to user)\n\n"
"Reminder: Always seek professional help, such as a doctor."
)
try:
response = genai.GenerativeModel("gemini-1.5-flash").generate_content(prompt)
return markdown2.markdown(response.text.strip())
except Exception as e:
return f"Error: {e}"
def predict_image(image):
image_resized = image.resize((224, 224))
image_array = np.array(image_resized).astype(np.float32) / 255.0
image_array = np.expand_dims(image_array, axis=0)
predictions = model.signatures['serving_default'](tf.convert_to_tensor(image_array, dtype=tf.float32))['output_0']
# Highest prediction
top_index = np.argmax(predictions.numpy(), axis=1)[0]
top_label = labels[top_index]
top_probability = predictions.numpy()[0][top_index]
explanation = get_disease_detail(top_label)
return {top_label: top_probability}, explanation
# Example images
example_images = [
["exp_eye_images/0_right_h.png"],
["exp_eye_images/03fd50da928d_dr.png"],
["exp_eye_images/108_right_h.png"],
["exp_eye_images/1062_right_c.png"],
["exp_eye_images/1084_right_c.png"],
["exp_eye_images/image_1002_g.jpg"]
]
# Custom CSS for HTML height
css = """
.scrollable-html {
height: 200px; /* Adjust this height as needed */
overflow-y: auto; /* Enable vertical scrolling */
border: 1px solid #ccc; /* Optional: border for visibility */
padding: 10px; /* Optional: padding for content */
box-sizing: border-box; /* Include padding in height calculation */
}
"""
# Gradio Interface
interface = gr.Interface(
fn=predict_image,
inputs=gr.Image(type="pil"),
outputs=[
gr.Label(num_top_classes=1, label="Prediction"),
gr.HTML(label="Explanation", elem_classes=["scrollable-html"])
],
examples=example_images,
title="Eye Diseases Classifier",
description=(
"Upload an image of an eye fundus, and the model will predict it.\n\n"
"**Disclaimer:** This model is intended as a form of learning process in the field of health-related machine learning and was trained with a limited amount and variety of data with a total of about 4000 data, so the prediction results may not always be correct. There is still a lot of room for improvisation on this model in the future."
),
allow_flagging="never",
css=css
)
interface.launch(share=True)
|